Triple
T15631089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fairly Legal |
E375812
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Michael Trucco |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Michael Trucco | Statement: [Fairly Legal, starring, Michael Trucco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Trucco Context triple: [Fairly Legal, starring, Michael Trucco]
-
A.
Michael Trucco
chosen
Michael Trucco is an American actor best known for his roles in television series such as Battlestar Galactica, How I Met Your Mother, and various genre and drama shows.
-
B.
Michael Nuccio
Michael Nuccio is an actor known for appearing in the psychological thriller film "In the Cut."
-
C.
Marc Trasolini
Marc Trasolini is a Canadian professional basketball player and standout former forward for Santa Clara University who went on to play internationally, particularly in Japan’s B.League.
-
D.
Anthony Di Iorio
Anthony Di Iorio is a Canadian entrepreneur and early cryptocurrency pioneer best known as one of the co-founders of Ethereum and founder of the blockchain company Decentral.
-
E.
Michael Raffetto
Michael Raffetto was an American radio actor best known for his prominent roles in classic radio dramas during the 1930s and 1940s.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04eb536348190b93ed3c178d1ffb8 |
completed | April 16, 2026, 2:51 a.m. |
Created at: April 10, 2026, 4:14 a.m.